Summary of Year-One Effort of the RCMI Consortium to Enhance Research Capacity and Diversity with Data Science

Christopher S. Awad (), Youping Deng, John Kwagyan, Abiel Roche-Lima, Paul B. Tchounwou, Qingguo Wang and Muhammed Y. Idris
Additional contact information
Christopher S. Awad: Department of Medicine, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322, USA
Youping Deng: Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI 96813, USA
John Kwagyan: Department of Community Health and Family Medicine, Howard University College of Medicine, 520 W St, Washington, DC 20059, USA
Abiel Roche-Lima: Department of Bioinformatics, Medical Science Campus, University of Puerto Rico, CCHRD-RCMI, P.O. Box 365067, San Juan, PR 00936, USA
Paul B. Tchounwou: Department of Biology, Jackson State University, 1400 J R Lynch Street, Jackson, MS 39217, USA
Qingguo Wang: Department of Computer Science & Data Science, School of Applied Computational Sciences, Meharry Medical College, 1005 Dr. D.B. Todd Jr. Blvd., Nashville, TN 37208, USA
Muhammed Y. Idris: Department of Medicine, Clinical Research Center, Morehouse School of Medicine, 720 Westview Dr SW, Atlanta, GA 30310, USA

IJERPH, 2022, vol. 20, issue 1, 1-12

Abstract: Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce.

Keywords: health disparities; training; data science; diversity; biomedical workforce (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/20/1/279/pdf (application/pdf)
https://www.mdpi.com/1660-4601/20/1/279/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:20:y:2022:i:1:p:279-:d:1013889

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().